There’s no doubt that our world faces complex challenges, from a warming climate to violent uprisings to political instability to outbreaks of disease. The number of these crises currently unfolding – in combination with persistent economic uncertainty – has led many leaders to lament the rise of volatility, uncertainty, complexity, and ambiguity. Resilience and adaptability, it seems, are our only recourse.

But what if such destabilizing events could be predicted ahead of time? What actions could leaders take if early warning signs are easier to spot? Just this decade, we have finally reached the critical amount of data and computer power needed to create such tools.

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For instance, recently my fellow data scientists and I developed algorithms that accurately predicted the first cholera outbreak in 130 years. The pattern that our system inferred was that cholera outbreaks in land-locked areas are more likely to occur following storms, especially when preceded by a long drought up to two years before. The pattern only occurs in countries with low GDP that have low concentration of water in the area. This is extremely surprising, as cholera is a water-born disease and one would expect it to happen in areas with a high water concentration. (One possible explanation might lie in how cholera infections are treated: if prompt dehydration treatment is supplied, cholera mortality rates drop from 50% to less that 1%. Therefore, it might be that in areas with enough clean water the epidemic did not break out.)

The implication of such predictions, automatically inferred by an-ever-updating statistical system, is that medical teams can be alerted as far as two years in advance that there’s a risk of a cholera epidemic in a specific location, and can send in clean water and save lives.

Other epidemics can be predicted in a similar way. Ebola is still rare enough that statistical patterns are tough to infer. Nevertheless, using human casualty knowledge mined from medical publications, in conjunction with recurring events, a prominent pattern for Ebola outbreaks does emerge.

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We have used the same approach to model the likelihood of outbreaks of violence. Our system predicted riots in Syria and Sudan, and their locations, by noticing that riots are more likely in non-democratic regions with growing GDPs yet low per-person income, when a previously subsidized product’s price is lifted, causing student riots and clashes with police.

The algorithm also predicted genocide by identifying that those events happen with higher probability if leaders or prominent people in the country dehumanize the minority, specifically when they refer to minority members as pests. One such example is the genocide in Rwanda. Years before 4,000 Tutsis were murdered in Kivumu, Hutu leaders such as Kivumu mayor Gregoire Ndahimana referred to the minority Tutsis as inyenzi (cockroaches). From this and other historical data, our algorithm inferred that genocide probability almost quadruples if: a) a person or a group describes a minority group (as defined by census and UN data) as either a non-mammal or as a disease-spreading animal, such as mice, and b) the speaker does so 3-5 years before they’ve been are reported in the news a minimum of few dozen times and have a local language Wikipedia entry about them.

After an empirical analysis of thousands of events happening in the last century, we’ve observed that our system identifies 30%-60% of upcoming events with 70%-90% accuracy. That’s no crystal ball. But it’s far, far better than what humans have had before.